The quality of digital images taken with optical cameras can be degraded by a number of factors, including motion blur, lack of focus, poor lighting, distortion, or other artifacts. One process for identifying and correcting the degradation of images is deconvolution. Deconvolution attempts to quantify one or more factors which degrade the image and then mathematically reverse the effects these factors have on the image. However, because of the difficulty in accurately quantifying the factors which degrade the image, deconvolution is often mathematically intense process which works best with highly calibrated systems which are used in controlled environments. The proliferation of inexpensive digital cameras and cameras on mobile devices has dramatically increased the number of images taken. However, it is particularly difficult to apply blind image deconvolution on these platforms because of the lack of available computing power, the wide range of imaging environments, optical limitations, and other factors.